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💡 Welcome to BIPro #118

Your weekly signal on analytics, automation, and the evolution of business intelligence.

This week, we spotlight Sagar Lad, a Data & AI Solution Architect with a knack for turning abstract AI concepts into clear, applied insights. In his latest piece, “What Are AI Agents? A Simple Guide,” Sagar explores how intelligent agents are moving beyond static automation, transforming data-driven systems into adaptive, goal-oriented entities.

From customer service bots that learn through interaction to autonomous systems that optimize decisions in real time, Sagar breaks down the internal logic of AI agents: how they perceive data, reason across states, and act with context awareness. It’s a concise, structured read that bridges AI theory with practical BI applications, offering a glimpse into how agent-based intelligence will reshape workflows, analytics, and decision automation.

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📈 Workshop Spotlight: Algorithmic Trading with Python (Cohort 2)
On December 12, join Jason Strimpel for a hands-on workshop on building algorithmic trading systems with Python, pandas, VectorBT, and Interactive Brokers API. Explore how data-driven strategy design, backtesting, and real-time execution come together, an ideal session for BI professionals looking to extend their analytical skills into financial modeling and automation.

Let’s dive in 👇

Cheers,

Merlyn Shelley

Growth Lead, Packt

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What Are AI Agents? A Simple Guide by Sagar Lad

Artificial Intelligence (AI) is transforming the way we live and work. One of the most exciting developments in this field is the rise ofAI agents. But what exactly are they? Let’s break it down in a way that’s easy to understand.

What Is an AI Agent?

AnAI agentis a computer program that canperceive its environment,make decisions, andtake actionsto achieve a goal. Think of it like a digital assistant that doesn’t just follow instructions — it learns, adapts, and acts intelligently.

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Task Execution Flow by AI Agents

Example:

Imagine a robot vacuum cleaner. It senses where dirt is, avoids obstacles, and decides the best path to clean your room. That’s an AI agent in action!

Internal Working of an AI Agent

1. Perception (Input Layer)

The agent receives data from its environment.
This could be sensor data (for robots), user input (for chatbots), or digital signals (for trading bots).
Internally, this data is processed into a format the agent can understand often as structured data or features.

Example:A self-driving car uses cameras and sensors to detect pedestrians, traffic lights, and road signs.

2. Knowledge Base / State Representation

The agent maintains a model of the world or its environment.
This could be a simple memory of past actions or a complex map of the surroundings.
It helps the agent understand context and make informed decisions.

Example:A chess-playing agent remembers the current board state and possible moves.

3. Decision-Making (Reasoning Engine)

Based on the perceived data and internal state, the agent decides what to do next.

This can involve:

Rule-based logic(if-then rules)
Search algorithms(finding the best path or solution)
Machine learning models(predicting outcomes)
Reinforcement learning(learning from trial and error)

Example:A recommendation agent uses a machine learning model to suggest movies based on your viewing history.

4. Action (Output Layer)

The agent performs an action in the environment.
This could be sending a message, moving a robot arm, or updating a database.

Example:A chatbot replies to a user query with a helpful answer.

5. Learning

Some agents improve over time by learning from feedback or new data.
Supervised learning(learning from labeled data)
Unsupervised learning(finding patterns)
Reinforcement learning(learning from rewards and penalties)

Example:A game-playing agent gets better by playing thousands of games and learning which strategies win.

🔄 The Agent Loop

Here’s a simplified loop that most AI agents follow:

Perceive → Analyze → Decide → Act → Learn (optional) → Repeat

👉 Continue reading the full article on the Packt Medium handle.

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See you next time!